For this reason, machine learning techniques have been investigated for answer verbalization. These techniques, in turn, depend primarily on large-scale training data. At present, the datasets that can be applied for answer verbalization tasks are mostly RDF verbalization datasets like WebNLG [1]}.
Whereas in VQuAnDa dataset [2]} (we discuss this dataset in detail in section ), they verbalized the answer based on the SPARQL query.
Using such RDF/Query verbalization technique, we can verbalize the triple, but they might be not apt for the answer representation as they do not take the question context into account.
As an example, consider the upper question in Figure REF . In this case, the triple verbalization \(A_T\)  `Suzi Perry is the unmarried partner of Nicki Knowles.' is less suitable than the human like response "Suzi Perry was girlfriend of Nicki Knowles", which uses the word "girlfriend" from the user query.
In order to spur research into such context-driven answer verbalizations for KGQA, we present the VANiLLa: `Verbalized Answers in Natural Language at Large scale` where the verbalized answer sentences are generated by using the context of question to encourage human-like responses.
